Syntactic n-grams in Computational Linguistics

94 Pages·2019·2.179 MB·English

by  Grigori Sidorov

SPRINGER BRIEFS IN COMPUTER SCIENCE
Grigori Sidorov
Syntactic
n-grams in
Computational
Linguistics
123
SpringerBriefs in Computer Science
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Shashi Shekhar, University of Minnesota, Minneapolis, MN, USA
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David Padua, University of Illinois Urbana-Champaign, Urbana, IL, USA
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Grigori Sidorov
Syntactic n-grams
in Computational Linguistics
Grigori Sidorov
Instituto Politécnico Nacional
Centro de Investigación en Computación
Mexico City, Mexico
ISSN 2191-5768 ISSN 2191-5776 (electronic)
SpringerBriefs in Computer Science
ISBN 978-3-030-14770-9 ISBN 978-3-030-14771-6 (eBook)
https://doi.org/10.1007/978-3-030-14771-6
Library of Congress Control Number: 2019935141
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Preface
This is a new substantially revised edition of the book, where we discuss the use of
syntactic information (represented as syntactic n-grams) in the tasks of computational
linguistics related to application of machine learning methods. We substantially
revised and completed the book adding several new chapters.
The previous edition of this book was published several years ago in Spanish
under the name of Non-linear Construction of N-Grams in Computational
Linguistics: Syntactic, Filtered, and Generalized N-Grams, and we got very positive
feedback from many researchers and students, who complimented us saying the
book presents really novel idea, describes this idea in a very clear way, and besides
presents the background in the very transparent manner, which makes it possible for
many persons clarifying the procedures applied in the modern computational
linguistics.
We made substantial changes in the book as compared to the previous edition
adding several chapters, and we also added more examples in English. We also
clarified the relationship between continuous and noncontinuous syntactic n-grams.
Let us remind that the concept suggested in the book, syntactic n-grams, is a
universal concept, which can be applied in any language.
We are delighted to present to the reader the modified book now in its English
version.
This book is about a new approach in the field of computational linguistics
related to the idea of constructing n-grams in a nonlinear manner, while the
traditional approach consists in using the data from the surface structure of texts,
i.e., the linear structure.
In this book, we propose and systematize the concept of syntactic n-grams,
which allows using syntactic information within the automatic text processing
methods related to classification or clustering. It is a very interesting example of
application of linguistic information in the automatic (computational) methods.
Roughly speaking, the suggestion is to follow syntactic trees and construct n-grams
based on paths in these trees. There are several types of nonlinear n-grams; future
work should determine which types of n-grams are more useful in which natural
language processing (NLP) tasks. For clarification of the basic concept for the
v
vi Preface
reader, we dedicate the first part of the book to explanation of basic concepts of
computational linguistics and machine learning (vector space model, tf-idf, etc.)
and explain the general scheme of design of the experiment in this field.
The book, first and foremost, is intended for specialists in the field of
computational linguistics. However, we made an effort to explain in a clear manner
how to use n-grams; we provide a large number of examples, and, therefore, we
believe that the book is also useful for graduate students who already have some
previous background in the field.
We want to emphasize that no profound knowledge of computing or mathematics
is required; the proposed concepts are intuitively very clear; we use very few
formulas, and if they appear, they are explained in detail.
Mexico City, Mexico Grigori Sidorov
Introduction
In this book, we discuss a novel idea in the field of computational linguistics: the
construction of n-grams in a nonlinear manner.
First, we discuss the concept of the vector space model in detail – a conceptual
framework for comparison of any type of objects and then its application to the text
processing-related tasks, i.e., its use in computational linguistics. Concepts related
to word frequency (tf-idf) are discussed, and the latent semantic analysis that allows
reducing the number of dimensions is briefly presented.
We mention important concepts concerning the design of experiments in
computational linguistics and describe the typical scheme of experiment in this
area.
We present the concept of traditional (linear) n-grams and compare it with the
concept of n-grams obtained in a nonlinear manner: syntactic, filtered, and
generalized n-grams.
Syntactic n-grams are n-grams constructed by following paths in syntactic trees.
The great advantage of syntactic n-grams is that they allow introducing pure
linguistic (syntactic) information into machine learning methods. The disadvantage
is that syntactic parsing is required for their construction (note that already there are
syntactic parsers available for many languages).
We consider both continuous and noncontinuous syntactic n-grams. When
constructing continuous syntactic n-grams, bifurcations (returns, interruptions,
ramification) in the syntactic paths are not allowed; when removing this constraint,
noncontinuous syntactic n-grams are obtained: sub-trees of length n with bifurcations
of a syntax tree are considered. It is noteworthy that we can unite these two types of
sn-grams: continuous and noncontinuous syntactic n-grams are complete syntactic
n-grams; it is all sub-trees of length n.
We propose a metalanguage for the representation of noncontinuous syntactic
n-grams, i.e., a formal way to represent a noncontinuous syntactic n-gram using
brackets and commas, e.g., “a b [c [d, e], f].” In this case, brackets and commas are
a part of the sn-grams.
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viii Introduction
In this book, we also present several examples of construction of continuous and
noncontinuous syntactic n-grams for syntactic trees obtained using the FreeLing
and the Stanford parsers.
We show that the application of syntactic n-grams in one of the traditional
computational linguistics tasks, the task of authorship attribution, gives better
results than using traditional n-grams.
Finally, we present several ideas concerning the other types of nonlinearly
constructed n-grams:
1. Filtered n-grams: a filter of words or features is built using a certain criterion
before constructing n-grams; then n-grams are constructed using only the
elements that passed through the filter.
2. Generalized n-grams: words “are generalized” using lexical relations, especially
synonymy and hypernymy; in this way, the set of elements used for constructing
n-grams is reduced.
Many experimental studies are required in order to determine which construction
parameters of continuous and noncontinuous, filtered, and generalized n-grams are
the best and for which existing tasks in computational linguistics.
The book systematizes the recent author’s proposals on the nonlinear construction
of n-grams and their use in vector space model; thereby, some parts of the book are
based on the author’s previous works published in various journals and conferences
with updates and necessary adjustments.
Work is done under the partial support of the Mexican government (CONACYT,
SNI); Mexico City government (ICYT-DF PICCO10-120 project); National
Polytechnic Institute, Mexico (projects SIP, COFAA); CONACYT project 240844;
and FP7 PEOPLE-2010 IRSES: Web Information Quality Evaluation Initiative
(WIQ-EI) European Commission project 269180.
Contents
Part I Vector Space Model in the Analysis of Similarity between Texts
1 Formalization in Computational Linguistics . . . . . . . . . . . . . . . . . . . . . 3
2 Vector Space Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3 Vector Space Model for Texts and the tf-idf Measure . . . . . . . . . . . . . . 11
4 Latent Semantic Analysis (LSA): Reduction of Dimensions . . . . . . . . . 17
5 Design of Experiments in Computational Linguistics . . . . . . . . . . . . . . 21
6 Example of Application of n-grams: Authorship Attribution
Using Syllables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
7 Deep Learning and Vector Space Model . . . . . . . . . . . . . . . . . . . . . . . . . 41
Part II Non-linear Construction of n-grams
8 Syntactic n-grams: The Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
9 Types of Syntactic n-grams According to their Components . . . . . . . . 59
10 Continuous and Noncontinuous Syntactic n-grams. . . . . . . . . . . . . . . . 63
11 Metalanguage of Syntactic n-gram Representation . . . . . . . . . . . . . . . . 69
12 Examples of Construction of Non- continuous Syntactic n-grams . . . . 71
13 Automatic Analysis of Authorship Using Syntactic n-grams . . . . . . . . 79
14 Filtered n-grams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
15 Generalized n-grams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
ix